A current approach to concatenative speech synthesis is to use a very large database for recorded speech that has been segmented and labeled with prosodic and spectral characteristics, such as the fundamental frequency (F0) for voiced speech, the energy or gain of the signal, and the spectral distribution of the signal (i.e., how much of the signal is present at any given frequency). The database contains multiple instances of speech sounds. This multiplicity permits the possibility of having units in the database that are much less stylized than would occur in a diphone database (a “diphone” being defined as the second half of one phoneme followed by the initial half of the following phoneme, a diphone database generally containing only one instance of any given diphone). Therefore, the possibility of achieving natural speech is enhanced with the “large database” approach.
For good quality synthesis, this database technique relies on being able to select the “best” units from the database—that is, the units that are closest in character to the prosodic specification provided by the speech synthesis system, and that have a low spectral mismatch at the concatenation points between phonemes. The “best” sequence of units may be determined by associating a numerical cost in two different ways. First, a “target cost” is associated with the individual units in isolation, where a lower cost is associated with a unit that has characteristics (e.g., F0, gain, spectral distribution) relatively close to the unit being synthesized, and a higher cost is associated with units having a higher discrepancy with the unit being synthesized. A second cost, referred to as the “concatenation cost”, is associated with how smoothly two contiguous units are joined together. For example, if the spectral mismatch between units is poor, there will be a higher concatenation cost.
Thus a set of candidate units for each position in the desired sequence can be formulated, with associated target costs and concatenative costs. Estimating the best (lowest-cost) path through the network is then performed using, for example, a Viterbi search. The chosen units may then concatenated to form one continuous signal, using a variety of different techniques.
While such database-driven systems may produce a more natural sounding voice quality, to do so they require a great deal of computational resources during the synthesis process. Accordingly, there remains a need for new methods and systems that provide natural voice quality in speech synthesis while reducing the computational requirements.